Fatima Zahra Marok (1), Daniel Wojtyniak (2), Simeon Rüdesheim (1,3), Laura Maria Fuhr (1), Matthias Schwab (3,4,5) Thorsten Lehr (1)
(1) Department of Clinical Pharmacy, Saarland University, Saarbrücken, Germany (2) Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Germany (3) Dr. Margarete Fischer-Bosch-Institut of Clinical Pharmacology, Stuttgart, Germany (4) Departments of Clinical Pharmacology, and of Biochemistry and Pharmacy, University Tuebingen, Tuebingen, Germany (5) Cluster of excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University Tuebingen, Tuebingen, Germany
Introduction: The racemate mirtazapine, used in treatment of major depressive disorders, is metabolized by several cytochrome P450 (CYP) enzymes, predominantly CYP1A2, CYP2D6 and CYP3A4 [1,2]. Here, differences in the metabolism of its enantiomers S- and R-mirtazapine have been observed, with the S-mirtazapine being predominately metabolized by CYP1A2 and CYP2D6, while R-mirtazapine is mainly transformed by CYP3A4 [2]. Metabolism of both enantiomers results in the formation of their racemic metabolite N-desmethylmirtazapine [2]. Particularly concerning CYP2D6-mediated metabolism, drug-gene interactions have been observed for mirtazapine administration, resulting in a 79% increase and a 33% decrease of the area under the plasma concentration-time curve (AUC) of S-mirtazapine in CYP2D6 poor and ultrarapid metabolizer, respectively [3]. Moreover, drug-drug interactions (DDIs) of mirtazapine as a victim drug, involving perpetrator drugs targeting CYP1A2, CYP2D6 and CYP3A4 have been reported in literature [4–8]. For instance, DDIs with ketoconazole, a potent CYP1A2, CYP2D6 and CYP3A4 inhibitor, led to an increase of 45% in AUC of mirtazapine, while carbamazepine, a strong CYP1A2 and CYP3A4 inducer, decreased mirtazapine AUC by 60% [5,7].
Objectives:
- To build a physiologically based pharmacokinetic (PBPK) model for racemic mirtazapine and its metabolite N-desmethylmirtazapine, as well as their enantiomers.
- To predict CYP2D6 DGIs for S-mirtazapine and racemic mirtazapine.
- To predict DDIs of mirtazapine as victim drug with the perpetrator drugs ketoconazole, cimetidine and paroxetine as CYP inhibitors, as well as with carbamazepine and phenytoin as CYP inducers.
Methods: The PBPK models were developed in PK-Sim® and MoBi® (version 11.0) as part of the Open Systems Pharmacology Suite (https://open-systems-pharmacology.org/) [9]. For model development, data were extracted from the literature, including physicochemical properties, data on biochemical processes, and clinical study data for all compounds. The latter were divided into a training and test data set for model building and evaluation, respectively. Except for the phenytoin PBPK model, remaining PBPK models for the DDI perpetrator drugs were derived from literature. Mean relative deviations (MRD) and geometric mean fold errors (GMFE) were used for evaluation of the overall model performance, as well as effect model performance of DGIs and DDIs.
Results: Whole-body PBPK models for mirtazapine and its metabolite N-desmethylmirtazapine, as well as their respective enantiomers were developed. Overall, 19 clinical studies with 23 plasma concentration-time profiles covering a dosing range of 7.5–75 mg or oral mirtazapine were used for model development. The presented parent metabolite PBPK model indicated a good predictive performance, with an overall MRD of 1.46 for predicted compared to observed (pred-obs) plasma concentration values, as well as GMFEs of 1.39 and 1.54 for pred-obs AUC and maximum plasma concentration (Cmax) values. Moreover, the model is capable to describe CYP2D6 DGIs with GMFEs of 1.24 (1.00–1.80) and 1.22 (1.01–1.61) of mean pred-obs AUC and Cmax values. Lastly, DDIs were well predicted with 1.17 (1.08–1.87) and 1.18 (1.02–1.56) of mean pred-obs AUC and Cmax, respectively.
Conclusions: Whole-body PBPK models of racemic mirtazapine its metabolite N-desmethylmirtazapine, as well as their enantiomers were developed. Additionally, the models include CYP2D6 DGI effects describing their enantioselective impact on S-mirtazapine. Moreover, several CYP-mediated DDIs with mirtazapine as victim were successfully captured with the presented PBPK models.
Funding: The project has received support from the project “Improve Safety in Polymedication by Managing Drug-Drug-Gene Interactions” (SafePolyMed). The SafePolyMed project receives funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101057639. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Matthias Schwab was supported by the Robert Bosch Stiftung (Stuttgart, Germany), a grant from the German Federal Ministry of Education and Research (BMBF, 031L0188D, “GUIDE-IBD”) and the DFG im Rahmen der Exzellenzstrategie des Bundes und der Länder-EXC 2180-390900677.
References:
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Reference: PAGE 32 (2024) Abstr 11047 [www.page-meeting.org/?abstract=11047]
Poster: Drug/Disease Modelling - Absorption & PBPK